Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations436876
Missing cells0
Missing cells (%)0.0%
Duplicate rows1205
Duplicate rows (%)0.3%
Total size in memory96.7 MiB
Average record size in memory232.0 B

Variable types

Numeric19
Text3
Categorical5
DateTime1

Alerts

NO2 Units has constant value "Parts per billion" Constant
O3 Units has constant value "Parts per million" Constant
SO2 Units has constant value "Parts per billion" Constant
CO Units has constant value "Parts per million" Constant
Dataset has 1205 (0.3%) duplicate rowsDuplicates
CO is highly overall correlated with CO 1st Max Value and 4 other fieldsHigh correlation
CO 1st Max Value is highly overall correlated with CO and 4 other fieldsHigh correlation
CO AQI is highly overall correlated with CO and 4 other fieldsHigh correlation
County Code is highly overall correlated with StateHigh correlation
NO2 is highly overall correlated with CO and 4 other fieldsHigh correlation
NO2 1st Max Value is highly overall correlated with CO and 4 other fieldsHigh correlation
NO2 AQI is highly overall correlated with CO and 4 other fieldsHigh correlation
O3 is highly overall correlated with O3 1st Max Value and 1 other fieldsHigh correlation
O3 1st Max Value is highly overall correlated with O3 and 1 other fieldsHigh correlation
O3 AQI is highly overall correlated with O3 and 1 other fieldsHigh correlation
SO2 is highly overall correlated with SO2 1st Max Value and 1 other fieldsHigh correlation
SO2 1st Max Value is highly overall correlated with SO2 and 1 other fieldsHigh correlation
SO2 AQI is highly overall correlated with SO2 and 1 other fieldsHigh correlation
State is highly overall correlated with County Code and 1 other fieldsHigh correlation
State Code is highly overall correlated with StateHigh correlation
NO2 1st Max Hour has 42037 (9.6%) zeros Zeros
O3 1st Max Hour has 22183 (5.1%) zeros Zeros
SO2 has 32672 (7.5%) zeros Zeros
SO2 1st Max Value has 34437 (7.9%) zeros Zeros
SO2 1st Max Hour has 79407 (18.2%) zeros Zeros
SO2 AQI has 95944 (22.0%) zeros Zeros
CO has 16904 (3.9%) zeros Zeros
CO 1st Max Value has 17024 (3.9%) zeros Zeros
CO 1st Max Hour has 194622 (44.5%) zeros Zeros
CO AQI has 17085 (3.9%) zeros Zeros

Reproduction

Analysis started2025-07-15 05:14:57.505639
Analysis finished2025-07-15 05:16:37.836871
Duration1 minute and 40.33 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

State Code
Real number (ℝ)

High correlation 

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.309507
Minimum1
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2025-07-15T05:16:37.994845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q16
median17
Q340
95-th percentile48
Maximum80
Range79
Interquartile range (IQR)34

Descriptive statistics

Standard deviation17.257025
Coefficient of variation (CV)0.77352789
Kurtosis-0.98260785
Mean22.309507
Median Absolute Deviation (MAD)11
Skewness0.5208059
Sum9746488
Variance297.80493
MonotonicityNot monotonic
2025-07-15T05:16:38.235611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
6 144109
33.0%
42 47278
 
10.8%
48 30807
 
7.1%
36 17624
 
4.0%
4 17469
 
4.0%
17 12536
 
2.9%
37 9282
 
2.1%
51 9107
 
2.1%
5 8832
 
2.0%
8 8801
 
2.0%
Other values (37) 131031
30.0%
ValueCountFrequency (%)
1 782
 
0.2%
2 494
 
0.1%
4 17469
 
4.0%
5 8832
 
2.0%
6 144109
33.0%
8 8801
 
2.0%
9 7483
 
1.7%
10 909
 
0.2%
11 6431
 
1.5%
12 6479
 
1.5%
ValueCountFrequency (%)
80 2383
 
0.5%
56 3261
 
0.7%
55 379
 
0.1%
53 241
 
0.1%
51 9107
 
2.1%
49 2167
 
0.5%
48 30807
7.1%
47 1459
 
0.3%
46 2078
 
0.5%
45 1634
 
0.4%

County Code
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.684636
Minimum1
Maximum650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2025-07-15T05:16:38.458066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q117
median59
Q397
95-th percentile201
Maximum650
Range649
Interquartile range (IQR)80

Descriptive statistics

Standard deviation79.47319
Coefficient of variation (CV)1.1086502
Kurtosis15.913892
Mean71.684636
Median Absolute Deviation (MAD)42
Skewness3.2124329
Sum31317297
Variance6315.9878
MonotonicityNot monotonic
2025-07-15T05:16:38.677475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 37312
 
8.5%
37 23868
 
5.5%
83 20759
 
4.8%
1 19903
 
4.6%
3 17048
 
3.9%
119 16834
 
3.9%
73 15469
 
3.5%
95 14323
 
3.3%
5 13346
 
3.1%
31 12142
 
2.8%
Other values (63) 245872
56.3%
ValueCountFrequency (%)
1 19903
4.6%
2 2383
 
0.5%
3 17048
3.9%
5 13346
 
3.1%
7 7745
 
1.8%
9 4223
 
1.0%
11 4885
 
1.1%
13 37312
8.5%
15 504
 
0.1%
17 5961
 
1.4%
ValueCountFrequency (%)
650 1181
 
0.3%
510 4525
1.0%
453 543
 
0.1%
309 3121
 
0.7%
209 5512
1.3%
201 10328
2.4%
191 237
 
0.1%
189 2156
 
0.5%
183 795
 
0.2%
163 10238
2.3%

Site Num
Real number (ℝ)

Distinct110
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1118.5401
Minimum1
Maximum9997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2025-07-15T05:16:38.875922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median60
Q31039
95-th percentile5005
Maximum9997
Range9996
Interquartile range (IQR)1030

Descriptive statistics

Standard deviation2003.438
Coefficient of variation (CV)1.7911187
Kurtosis6.6827008
Mean1118.5401
Median Absolute Deviation (MAD)57
Skewness2.5582112
Sum4.8866331 × 108
Variance4013763.9
MonotonicityNot monotonic
2025-07-15T05:16:39.024834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 19036
 
4.4%
2 17970
 
4.1%
3 14711
 
3.4%
10 14699
 
3.4%
3001 13441
 
3.1%
9 13211
 
3.0%
41 13097
 
3.0%
4 12330
 
2.8%
8 12288
 
2.8%
1002 11478
 
2.6%
Other values (100) 294615
67.4%
ValueCountFrequency (%)
1 4493
 
1.0%
2 17970
4.1%
3 14711
3.4%
4 12330
2.8%
5 19036
4.4%
6 6178
 
1.4%
7 10751
2.5%
8 12288
2.8%
9 13211
3.0%
10 14699
3.4%
ValueCountFrequency (%)
9997 3837
0.9%
9009 892
 
0.2%
9004 935
 
0.2%
9003 1791
 
0.4%
9002 1718
 
0.4%
8003 638
 
0.1%
8001 7544
1.7%
7003 858
 
0.2%
7002 215
 
< 0.1%
6001 349
 
0.1%
Distinct204
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.7 MiB
2025-07-15T05:16:39.352836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length75
Median length44
Mean length26.566895
Min length6

Characters and Unicode

Total characters11606439
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
2nd row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
3rd row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
4th row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
5th row1645 E ROOSEVELT ST-CENTRAL PHOENIX STN
ValueCountFrequency (%)
st 96592
 
4.7%
ave 55217
 
2.7%
street 45413
 
2.2%
blvd 39474
 
1.9%
37305
 
1.8%
e 34999
 
1.7%
road 34892
 
1.7%
n 34057
 
1.7%
rd 32467
 
1.6%
ca 18454
 
0.9%
Other values (626) 1622908
79.1%
2025-07-15T05:16:39.804623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1651979
 
14.2%
E 704604
 
6.1%
A 650681
 
5.6%
T 556664
 
4.8%
S 524368
 
4.5%
R 507805
 
4.4%
N 438297
 
3.8%
O 420071
 
3.6%
L 374416
 
3.2%
I 311340
 
2.7%
Other values (65) 5466214
47.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11606439
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1651979
 
14.2%
E 704604
 
6.1%
A 650681
 
5.6%
T 556664
 
4.8%
S 524368
 
4.5%
R 507805
 
4.4%
N 438297
 
3.8%
O 420071
 
3.6%
L 374416
 
3.2%
I 311340
 
2.7%
Other values (65) 5466214
47.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11606439
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1651979
 
14.2%
E 704604
 
6.1%
A 650681
 
5.6%
T 556664
 
4.8%
S 524368
 
4.5%
R 507805
 
4.4%
N 438297
 
3.8%
O 420071
 
3.6%
L 374416
 
3.2%
I 311340
 
2.7%
Other values (65) 5466214
47.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11606439
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1651979
 
14.2%
E 704604
 
6.1%
A 650681
 
5.6%
T 556664
 
4.8%
S 524368
 
4.5%
R 507805
 
4.4%
N 438297
 
3.8%
O 420071
 
3.6%
L 374416
 
3.2%
I 311340
 
2.7%
Other values (65) 5466214
47.1%

State
Categorical

High correlation 

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.7 MiB
California
144109 
Pennsylvania
47278 
Texas
30807 
New York
 
17624
Arizona
 
17469
Other values (42)
179589 

Length

Max length20
Median length17
Mean length9.1770869
Min length4

Characters and Unicode

Total characters4009249
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArizona
2nd rowArizona
3rd rowArizona
4th rowArizona
5th rowArizona

Common Values

ValueCountFrequency (%)
California 144109
33.0%
Pennsylvania 47278
 
10.8%
Texas 30807
 
7.1%
New York 17624
 
4.0%
Arizona 17469
 
4.0%
Illinois 12536
 
2.9%
North Carolina 9282
 
2.1%
Virginia 9107
 
2.1%
Arkansas 8832
 
2.0%
Colorado 8801
 
2.0%
Other values (37) 131031
30.0%

Length

2025-07-15T05:16:39.938928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
california 144109
28.8%
pennsylvania 47278
 
9.5%
texas 30807
 
6.2%
new 28416
 
5.7%
york 17624
 
3.5%
arizona 17469
 
3.5%
illinois 12536
 
2.5%
north 12037
 
2.4%
carolina 10916
 
2.2%
virginia 9107
 
1.8%
Other values (41) 169950
34.0%

Most occurring characters

ValueCountFrequency (%)
a 629632
15.7%
i 514310
12.8%
n 414203
10.3%
o 320003
 
8.0%
r 269817
 
6.7%
l 267354
 
6.7%
s 183786
 
4.6%
C 180123
 
4.5%
e 166246
 
4.1%
f 152923
 
3.8%
Other values (36) 910852
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4009249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 629632
15.7%
i 514310
12.8%
n 414203
10.3%
o 320003
 
8.0%
r 269817
 
6.7%
l 267354
 
6.7%
s 183786
 
4.6%
C 180123
 
4.5%
e 166246
 
4.1%
f 152923
 
3.8%
Other values (36) 910852
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4009249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 629632
15.7%
i 514310
12.8%
n 414203
10.3%
o 320003
 
8.0%
r 269817
 
6.7%
l 267354
 
6.7%
s 183786
 
4.6%
C 180123
 
4.5%
e 166246
 
4.1%
f 152923
 
3.8%
Other values (36) 910852
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4009249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 629632
15.7%
i 514310
12.8%
n 414203
10.3%
o 320003
 
8.0%
r 269817
 
6.7%
l 267354
 
6.7%
s 183786
 
4.6%
C 180123
 
4.5%
e 166246
 
4.1%
f 152923
 
3.8%
Other values (36) 910852
22.7%

County
Text

Distinct133
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.7 MiB
2025-07-15T05:16:40.296448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length21
Median length14
Mean length8.7714592
Min length3

Characters and Unicode

Total characters3832040
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaricopa
2nd rowMaricopa
3rd rowMaricopa
4th rowMaricopa
5th rowMaricopa
ValueCountFrequency (%)
santa 27915
 
4.7%
san 24878
 
4.2%
los 23405
 
3.9%
angeles 23405
 
3.9%
contra 21003
 
3.5%
costa 21003
 
3.5%
barbara 20759
 
3.5%
diego 12783
 
2.1%
maricopa 11655
 
2.0%
orange 11240
 
1.9%
Other values (141) 398773
66.8%
2025-07-15T05:16:40.785001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 499182
 
13.0%
o 299742
 
7.8%
n 282079
 
7.4%
e 273030
 
7.1%
r 266409
 
7.0%
s 190712
 
5.0%
t 188603
 
4.9%
i 188014
 
4.9%
l 180152
 
4.7%
160437
 
4.2%
Other values (41) 1303680
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3832040
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 499182
 
13.0%
o 299742
 
7.8%
n 282079
 
7.4%
e 273030
 
7.1%
r 266409
 
7.0%
s 190712
 
5.0%
t 188603
 
4.9%
i 188014
 
4.9%
l 180152
 
4.7%
160437
 
4.2%
Other values (41) 1303680
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3832040
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 499182
 
13.0%
o 299742
 
7.8%
n 282079
 
7.4%
e 273030
 
7.1%
r 266409
 
7.0%
s 190712
 
5.0%
t 188603
 
4.9%
i 188014
 
4.9%
l 180152
 
4.7%
160437
 
4.2%
Other values (41) 1303680
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3832040
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 499182
 
13.0%
o 299742
 
7.8%
n 282079
 
7.4%
e 273030
 
7.1%
r 266409
 
7.0%
s 190712
 
5.0%
t 188603
 
4.9%
i 188014
 
4.9%
l 180152
 
4.7%
160437
 
4.2%
Other values (41) 1303680
34.0%

City
Text

Distinct144
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.7 MiB
2025-07-15T05:16:41.082606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length48
Median length24
Mean length9.6889071
Min length4

Characters and Unicode

Total characters4232851
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhoenix
2nd rowPhoenix
3rd rowPhoenix
4th rowPhoenix
5th rowPhoenix
ValueCountFrequency (%)
city 45266
 
6.2%
not 34619
 
4.8%
in 34619
 
4.8%
a 34619
 
4.8%
san 20104
 
2.8%
new 16633
 
2.3%
york 15674
 
2.2%
park 11605
 
1.6%
angeles 10879
 
1.5%
los 10879
 
1.5%
Other values (170) 491200
67.6%
2025-07-15T05:16:41.489256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 358392
 
8.5%
a 357029
 
8.4%
e 327336
 
7.7%
n 297772
 
7.0%
t 292653
 
6.9%
289221
 
6.8%
i 266797
 
6.3%
l 215837
 
5.1%
r 212101
 
5.0%
s 202330
 
4.8%
Other values (43) 1413383
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4232851
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 358392
 
8.5%
a 357029
 
8.4%
e 327336
 
7.7%
n 297772
 
7.0%
t 292653
 
6.9%
289221
 
6.8%
i 266797
 
6.3%
l 215837
 
5.1%
r 212101
 
5.0%
s 202330
 
4.8%
Other values (43) 1413383
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4232851
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 358392
 
8.5%
a 357029
 
8.4%
e 327336
 
7.7%
n 297772
 
7.0%
t 292653
 
6.9%
289221
 
6.8%
i 266797
 
6.3%
l 215837
 
5.1%
r 212101
 
5.0%
s 202330
 
4.8%
Other values (43) 1413383
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4232851
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 358392
 
8.5%
a 357029
 
8.4%
e 327336
 
7.7%
n 297772
 
7.0%
t 292653
 
6.9%
289221
 
6.8%
i 266797
 
6.3%
l 215837
 
5.1%
r 212101
 
5.0%
s 202330
 
4.8%
Other values (43) 1413383
33.4%
Distinct5996
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size6.7 MiB
Minimum2000-01-01 00:00:00
Maximum2016-05-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-15T05:16:41.641637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:41.792710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

NO2 Units
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.7 MiB
Parts per billion
436876 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters7426892
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion 436876
100.0%

Length

2025-07-15T05:16:41.910890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-15T05:16:41.984102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
parts 436876
33.3%
per 436876
33.3%
billion 436876
33.3%

Most occurring characters

ValueCountFrequency (%)
r 873752
11.8%
i 873752
11.8%
l 873752
11.8%
873752
11.8%
P 436876
 
5.9%
s 436876
 
5.9%
t 436876
 
5.9%
a 436876
 
5.9%
p 436876
 
5.9%
b 436876
 
5.9%
Other values (3) 1310628
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7426892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 873752
11.8%
i 873752
11.8%
l 873752
11.8%
873752
11.8%
P 436876
 
5.9%
s 436876
 
5.9%
t 436876
 
5.9%
a 436876
 
5.9%
p 436876
 
5.9%
b 436876
 
5.9%
Other values (3) 1310628
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7426892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 873752
11.8%
i 873752
11.8%
l 873752
11.8%
873752
11.8%
P 436876
 
5.9%
s 436876
 
5.9%
t 436876
 
5.9%
a 436876
 
5.9%
p 436876
 
5.9%
b 436876
 
5.9%
Other values (3) 1310628
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7426892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 873752
11.8%
i 873752
11.8%
l 873752
11.8%
873752
11.8%
P 436876
 
5.9%
s 436876
 
5.9%
t 436876
 
5.9%
a 436876
 
5.9%
p 436876
 
5.9%
b 436876
 
5.9%
Other values (3) 1310628
17.6%

NO2
Real number (ℝ)

High correlation 

Distinct31852
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.822498
Minimum-2
Maximum139.54167
Zeros2066
Zeros (%)0.5%
Negative225
Negative (%)0.1%
Memory size6.7 MiB
2025-07-15T05:16:42.068281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile1.416667
Q15.75
median10.73913
Q317.714286
95-th percentile31.173913
Maximum139.54167
Range141.54167
Interquartile range (IQR)11.964286

Descriptive statistics

Standard deviation9.5057158
Coefficient of variation (CV)0.74133107
Kurtosis2.7268468
Mean12.822498
Median Absolute Deviation (MAD)5.695652
Skewness1.3086848
Sum5601841.5
Variance90.358634
MonotonicityNot monotonic
2025-07-15T05:16:42.221504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2066
 
0.5%
7 738
 
0.2%
8 712
 
0.2%
6 692
 
0.2%
9 675
 
0.2%
1 670
 
0.2%
10 663
 
0.2%
11 663
 
0.2%
12 622
 
0.1%
4 612
 
0.1%
Other values (31842) 428763
98.1%
ValueCountFrequency (%)
-2 3
< 0.1%
-1.871429 1
 
< 0.1%
-1.833333 1
 
< 0.1%
-1.761111 1
 
< 0.1%
-1.736364 1
 
< 0.1%
-1.675 1
 
< 0.1%
-1.533333 1
 
< 0.1%
-1.454167 1
 
< 0.1%
-1.431579 1
 
< 0.1%
-1.4 1
 
< 0.1%
ValueCountFrequency (%)
139.541667 1
< 0.1%
135.333333 1
< 0.1%
135.1875 1
< 0.1%
123.333333 1
< 0.1%
113.083333 1
< 0.1%
110.136364 1
< 0.1%
107.545455 1
< 0.1%
105.5 1
< 0.1%
98.75 1
< 0.1%
98.130435 1
< 0.1%

NO2 1st Max Value
Real number (ℝ)

High correlation 

Distinct990
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.415907
Minimum-2
Maximum267
Zeros2074
Zeros (%)0.5%
Negative46
Negative (%)< 0.1%
Memory size6.7 MiB
2025-07-15T05:16:42.375746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile3.5
Q113
median24
Q335.7
95-th percentile53
Maximum267
Range269
Interquartile range (IQR)22.7

Descriptive statistics

Standard deviation16.000756
Coefficient of variation (CV)0.62955675
Kurtosis3.245602
Mean25.415907
Median Absolute Deviation (MAD)11
Skewness0.97893975
Sum11103600
Variance256.02419
MonotonicityNot monotonic
2025-07-15T05:16:42.522260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 7852
 
1.8%
22 7677
 
1.8%
23 7589
 
1.7%
26 7573
 
1.7%
24 7560
 
1.7%
21 7526
 
1.7%
18 7522
 
1.7%
16 7482
 
1.7%
19 7406
 
1.7%
28 7404
 
1.7%
Other values (980) 361285
82.7%
ValueCountFrequency (%)
-2 3
 
< 0.1%
-1.6 1
 
< 0.1%
-1.2 1
 
< 0.1%
-1.1 1
 
< 0.1%
-0.9 2
 
< 0.1%
-0.7 2
 
< 0.1%
-0.6 3
 
< 0.1%
-0.5 22
< 0.1%
-0.4 2
 
< 0.1%
-0.2 5
 
< 0.1%
ValueCountFrequency (%)
267 1
< 0.1%
262 1
< 0.1%
256 1
< 0.1%
251 1
< 0.1%
244 2
< 0.1%
241 1
< 0.1%
233 1
< 0.1%
231 1
< 0.1%
229 1
< 0.1%
225 1
< 0.1%

NO2 1st Max Hour
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.730919
Minimum0
Maximum23
Zeros42037
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2025-07-15T05:16:42.631443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median9
Q320
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)15

Descriptive statistics

Standard deviation7.8776331
Coefficient of variation (CV)0.67152736
Kurtosis-1.5354955
Mean11.730919
Median Absolute Deviation (MAD)8
Skewness0.013480958
Sum5124957
Variance62.057104
MonotonicityNot monotonic
2025-07-15T05:16:42.751995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
6 42257
 
9.7%
0 42037
 
9.6%
7 36015
 
8.2%
20 33254
 
7.6%
21 30855
 
7.1%
19 30136
 
6.9%
5 27077
 
6.2%
22 25408
 
5.8%
23 24980
 
5.7%
18 24760
 
5.7%
Other values (14) 120097
27.5%
ValueCountFrequency (%)
0 42037
9.6%
1 12750
 
2.9%
2 10210
 
2.3%
3 7492
 
1.7%
4 9985
 
2.3%
5 27077
6.2%
6 42257
9.7%
7 36015
8.2%
8 21088
4.8%
9 12886
 
2.9%
ValueCountFrequency (%)
23 24980
5.7%
22 25408
5.8%
21 30855
7.1%
20 33254
7.6%
19 30136
6.9%
18 24760
5.7%
17 13609
3.1%
16 5863
 
1.3%
15 3638
 
0.8%
14 3082
 
0.7%

NO2 AQI
Real number (ℝ)

High correlation 

Distinct129
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.89923
Minimum0
Maximum132
Zeros3162
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2025-07-15T05:16:42.878759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q112
median23
Q333
95-th percentile50
Maximum132
Range132
Interquartile range (IQR)21

Descriptive statistics

Standard deviation15.163655
Coefficient of variation (CV)0.63448301
Kurtosis1.5514349
Mean23.89923
Median Absolute Deviation (MAD)11
Skewness0.88768345
Sum10441000
Variance229.93644
MonotonicityNot monotonic
2025-07-15T05:16:43.016722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 19329
 
4.4%
8 19183
 
4.4%
19 10431
 
2.4%
15 10319
 
2.4%
17 10300
 
2.4%
21 10250
 
2.3%
20 10158
 
2.3%
18 10105
 
2.3%
16 10096
 
2.3%
14 10080
 
2.3%
Other values (119) 316625
72.5%
ValueCountFrequency (%)
0 3162
 
0.7%
1 6144
 
1.4%
2 6932
 
1.6%
3 7135
 
1.6%
4 8003
1.8%
5 8385
1.9%
6 8642
2.0%
7 9002
2.1%
8 19183
4.4%
9 9808
2.2%
ValueCountFrequency (%)
132 1
 
< 0.1%
131 1
 
< 0.1%
130 1
 
< 0.1%
129 1
 
< 0.1%
128 2
< 0.1%
127 1
 
< 0.1%
126 2
< 0.1%
125 1
 
< 0.1%
124 4
< 0.1%
123 3
< 0.1%

O3 Units
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.7 MiB
Parts per million
436876 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters7426892
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million 436876
100.0%

Length

2025-07-15T05:16:43.137562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-15T05:16:43.200666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
parts 436876
33.3%
per 436876
33.3%
million 436876
33.3%

Most occurring characters

ValueCountFrequency (%)
r 873752
11.8%
i 873752
11.8%
l 873752
11.8%
873752
11.8%
P 436876
 
5.9%
s 436876
 
5.9%
t 436876
 
5.9%
a 436876
 
5.9%
p 436876
 
5.9%
m 436876
 
5.9%
Other values (3) 1310628
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7426892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 873752
11.8%
i 873752
11.8%
l 873752
11.8%
873752
11.8%
P 436876
 
5.9%
s 436876
 
5.9%
t 436876
 
5.9%
a 436876
 
5.9%
p 436876
 
5.9%
m 436876
 
5.9%
Other values (3) 1310628
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7426892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 873752
11.8%
i 873752
11.8%
l 873752
11.8%
873752
11.8%
P 436876
 
5.9%
s 436876
 
5.9%
t 436876
 
5.9%
a 436876
 
5.9%
p 436876
 
5.9%
m 436876
 
5.9%
Other values (3) 1310628
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7426892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 873752
11.8%
i 873752
11.8%
l 873752
11.8%
873752
11.8%
P 436876
 
5.9%
s 436876
 
5.9%
t 436876
 
5.9%
a 436876
 
5.9%
p 436876
 
5.9%
m 436876
 
5.9%
Other values (3) 1310628
17.6%

O3
Real number (ℝ)

High correlation 

Distinct8190
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.026123975
Minimum0
Maximum0.095083
Zeros150
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2025-07-15T05:16:43.288330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.007875
Q10.017875
median0.025875
Q30.033917
95-th percentile0.045167
Maximum0.095083
Range0.095083
Interquartile range (IQR)0.016042

Descriptive statistics

Standard deviation0.011369268
Coefficient of variation (CV)0.43520437
Kurtosis-0.19348385
Mean0.026123975
Median Absolute Deviation (MAD)0.008
Skewness0.21752904
Sum11412.938
Variance0.00012926026
MonotonicityNot monotonic
2025-07-15T05:16:43.427561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.026458 658
 
0.2%
0.026 657
 
0.2%
0.027 649
 
0.1%
0.028292 641
 
0.1%
0.03 631
 
0.1%
0.024 630
 
0.1%
0.027458 629
 
0.1%
0.029 626
 
0.1%
0.0225 625
 
0.1%
0.023208 624
 
0.1%
Other values (8180) 430506
98.5%
ValueCountFrequency (%)
0 150
< 0.1%
4.2 × 10-54
 
< 0.1%
6.3 × 10-51
 
< 0.1%
8.3 × 10-511
 
< 0.1%
0.000105 1
 
< 0.1%
0.000125 15
 
< 0.1%
0.00013 1
 
< 0.1%
0.000158 1
 
< 0.1%
0.000167 19
 
< 0.1%
0.000182 1
 
< 0.1%
ValueCountFrequency (%)
0.095083 1
< 0.1%
0.086214 1
< 0.1%
0.085917 1
< 0.1%
0.08575 1
< 0.1%
0.085333 2
< 0.1%
0.084208 1
< 0.1%
0.083333 1
< 0.1%
0.083045 1
< 0.1%
0.081958 2
< 0.1%
0.081588 2
< 0.1%

O3 1st Max Value
Real number (ℝ)

High correlation 

Distinct134
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.039202504
Minimum0
Maximum0.141
Zeros150
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2025-07-15T05:16:43.560818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.016
Q10.029
median0.038
Q30.048
95-th percentile0.066
Maximum0.141
Range0.141
Interquartile range (IQR)0.019

Descriptive statistics

Standard deviation0.015343228
Coefficient of variation (CV)0.39138389
Kurtosis0.67338555
Mean0.039202504
Median Absolute Deviation (MAD)0.01
Skewness0.49192718
Sum17126.633
Variance0.00023541466
MonotonicityNot monotonic
2025-07-15T05:16:43.731662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.034 12234
 
2.8%
0.036 12218
 
2.8%
0.037 12170
 
2.8%
0.033 12169
 
2.8%
0.038 12105
 
2.8%
0.035 12091
 
2.8%
0.039 12045
 
2.8%
0.032 11729
 
2.7%
0.04 11520
 
2.6%
0.041 11514
 
2.6%
Other values (124) 317081
72.6%
ValueCountFrequency (%)
0 150
 
< 0.1%
0.001 277
 
0.1%
0.002 426
 
0.1%
0.003 506
 
0.1%
0.004 626
0.1%
0.005 748
0.2%
0.006 919
0.2%
0.007 1024
0.2%
0.008 1267
0.3%
0.009 1446
0.3%
ValueCountFrequency (%)
0.141 1
 
< 0.1%
0.14 1
 
< 0.1%
0.132 3
< 0.1%
0.131 1
 
< 0.1%
0.13 2
< 0.1%
0.129 1
 
< 0.1%
0.128 4
< 0.1%
0.127 2
< 0.1%
0.126 4
< 0.1%
0.125 2
< 0.1%

O3 1st Max Hour
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.170449
Minimum0
Maximum23
Zeros22183
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2025-07-15T05:16:43.862442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median10
Q311
95-th percentile19
Maximum23
Range23
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.0033161
Coefficient of variation (CV)0.39362236
Kurtosis3.3172214
Mean10.170449
Median Absolute Deviation (MAD)1
Skewness0.46903584
Sum4443225
Variance16.02654
MonotonicityNot monotonic
2025-07-15T05:16:43.981429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
10 119934
27.5%
9 86937
19.9%
11 76860
17.6%
8 33059
 
7.6%
12 26558
 
6.1%
0 22183
 
5.1%
13 10577
 
2.4%
7 9318
 
2.1%
23 7258
 
1.7%
14 5110
 
1.2%
Other values (14) 39082
 
8.9%
ValueCountFrequency (%)
0 22183
 
5.1%
1 1280
 
0.3%
2 1283
 
0.3%
3 1282
 
0.3%
4 1307
 
0.3%
5 1681
 
0.4%
6 3338
 
0.8%
7 9318
 
2.1%
8 33059
 
7.6%
9 86937
19.9%
ValueCountFrequency (%)
23 7258
1.7%
22 4936
1.1%
21 4958
1.1%
20 4480
1.0%
19 3554
0.8%
18 2780
 
0.6%
17 2400
 
0.5%
16 2549
 
0.6%
15 3254
0.7%
14 5110
1.2%

O3 AQI
Real number (ℝ)

High correlation 

Distinct125
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.049204
Minimum0
Maximum218
Zeros150
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2025-07-15T05:16:44.127299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q125
median33
Q342
95-th percentile74
Maximum218
Range218
Interquartile range (IQR)17

Descriptive statistics

Standard deviation19.779243
Coefficient of variation (CV)0.54867349
Kurtosis9.758742
Mean36.049204
Median Absolute Deviation (MAD)9
Skewness2.4391071
Sum15749032
Variance391.21844
MonotonicityNot monotonic
2025-07-15T05:16:44.281659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 24553
 
5.6%
36 19770
 
4.5%
25 18702
 
4.3%
42 14712
 
3.4%
19 13160
 
3.0%
33 12156
 
2.8%
32 12139
 
2.8%
30 12119
 
2.8%
29 11981
 
2.7%
28 11831
 
2.7%
Other values (115) 285753
65.4%
ValueCountFrequency (%)
0 150
 
< 0.1%
1 277
 
0.1%
2 425
 
0.1%
3 1062
 
0.2%
4 725
 
0.2%
5 913
 
0.2%
6 1120
 
0.3%
7 1267
0.3%
8 2844
0.7%
9 1737
0.4%
ValueCountFrequency (%)
218 1
 
< 0.1%
211 1
 
< 0.1%
210 1
 
< 0.1%
207 5
 
< 0.1%
206 8
< 0.1%
205 6
 
< 0.1%
204 6
 
< 0.1%
203 11
< 0.1%
202 16
< 0.1%
201 13
< 0.1%

SO2 Units
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.7 MiB
Parts per billion
436876 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters7426892
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion 436876
100.0%

Length

2025-07-15T05:16:44.403565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-15T05:16:44.467280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
parts 436876
33.3%
per 436876
33.3%
billion 436876
33.3%

Most occurring characters

ValueCountFrequency (%)
r 873752
11.8%
i 873752
11.8%
l 873752
11.8%
873752
11.8%
P 436876
 
5.9%
s 436876
 
5.9%
t 436876
 
5.9%
a 436876
 
5.9%
p 436876
 
5.9%
b 436876
 
5.9%
Other values (3) 1310628
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7426892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 873752
11.8%
i 873752
11.8%
l 873752
11.8%
873752
11.8%
P 436876
 
5.9%
s 436876
 
5.9%
t 436876
 
5.9%
a 436876
 
5.9%
p 436876
 
5.9%
b 436876
 
5.9%
Other values (3) 1310628
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7426892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 873752
11.8%
i 873752
11.8%
l 873752
11.8%
873752
11.8%
P 436876
 
5.9%
s 436876
 
5.9%
t 436876
 
5.9%
a 436876
 
5.9%
p 436876
 
5.9%
b 436876
 
5.9%
Other values (3) 1310628
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7426892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 873752
11.8%
i 873752
11.8%
l 873752
11.8%
873752
11.8%
P 436876
 
5.9%
s 436876
 
5.9%
t 436876
 
5.9%
a 436876
 
5.9%
p 436876
 
5.9%
b 436876
 
5.9%
Other values (3) 1310628
17.6%

SO2
Real number (ℝ)

High correlation  Zeros 

Distinct10471
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8816793
Minimum-2
Maximum321.625
Zeros32672
Zeros (%)7.5%
Negative6015
Negative (%)1.4%
Memory size6.7 MiB
2025-07-15T05:16:44.562163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile0
Q10.265217
median1
Q32.333333
95-th percentile7.0379075
Maximum321.625
Range323.625
Interquartile range (IQR)2.068116

Descriptive statistics

Standard deviation2.7618644
Coefficient of variation (CV)1.4677657
Kurtosis432.46178
Mean1.8816793
Median Absolute Deviation (MAD)0.852381
Skewness6.9666946
Sum822060.55
Variance7.6278949
MonotonicityNot monotonic
2025-07-15T05:16:44.707530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32672
 
7.5%
1 8823
 
2.0%
0.043478 4017
 
0.9%
2 3488
 
0.8%
0.041667 2812
 
0.6%
1.043478 2549
 
0.6%
0.086957 2389
 
0.5%
0.083333 2238
 
0.5%
0.125 2119
 
0.5%
0.5 2055
 
0.5%
Other values (10461) 373714
85.5%
ValueCountFrequency (%)
-2 2
< 0.1%
-1.725 1
 
< 0.1%
-1.721739 1
 
< 0.1%
-1.713043 1
 
< 0.1%
-1.704348 1
 
< 0.1%
-1.7 4
< 0.1%
-1.695833 2
< 0.1%
-1.686957 2
< 0.1%
-1.675 2
< 0.1%
-1.670833 3
< 0.1%
ValueCountFrequency (%)
321.625 1
< 0.1%
81.25 1
< 0.1%
58.565217 1
< 0.1%
56.083333 1
< 0.1%
53.5 1
< 0.1%
51.583333 1
< 0.1%
51.333333 1
< 0.1%
49.708333 1
< 0.1%
49.25 1
< 0.1%
47.833333 1
< 0.1%

SO2 1st Max Value
Real number (ℝ)

High correlation  Zeros 

Distinct810
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2522704
Minimum-2
Maximum351
Zeros34437
Zeros (%)7.9%
Negative1896
Negative (%)0.4%
Memory size6.7 MiB
2025-07-15T05:16:44.858368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile0
Q11
median2
Q36
95-th percentile20
Maximum351
Range353
Interquartile range (IQR)5

Descriptive statistics

Standard deviation8.9495734
Coefficient of variation (CV)1.7039437
Kurtosis69.029735
Mean5.2522704
Median Absolute Deviation (MAD)1.7
Skewness5.7421991
Sum2294590.9
Variance80.094864
MonotonicityNot monotonic
2025-07-15T05:16:45.003688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 49911
 
11.4%
2 39876
 
9.1%
0 34437
 
7.9%
3 28999
 
6.6%
4 21755
 
5.0%
5 16735
 
3.8%
6 13307
 
3.0%
7 10734
 
2.5%
8 9259
 
2.1%
0.3 7584
 
1.7%
Other values (800) 204279
46.8%
ValueCountFrequency (%)
-2 2
 
< 0.1%
-1.6 6
 
< 0.1%
-1.5 6
 
< 0.1%
-1.4 1
 
< 0.1%
-1.3 4
 
< 0.1%
-1.2 4
 
< 0.1%
-1.1 6
 
< 0.1%
-1 48
< 0.1%
-0.9 8
 
< 0.1%
-0.8 22
< 0.1%
ValueCountFrequency (%)
351 1
< 0.1%
327 1
< 0.1%
292 1
< 0.1%
257 1
< 0.1%
249 1
< 0.1%
248 1
< 0.1%
247 1
< 0.1%
245 1
< 0.1%
240 2
< 0.1%
237 1
< 0.1%

SO2 1st Max Hour
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7895055
Minimum0
Maximum23
Zeros79407
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2025-07-15T05:16:45.126114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median8
Q313
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.8031148
Coefficient of variation (CV)0.77400427
Kurtosis-0.81762989
Mean8.7895055
Median Absolute Deviation (MAD)5
Skewness0.40190241
Sum3839924
Variance46.282371
MonotonicityNot monotonic
2025-07-15T05:16:45.229536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 79407
18.2%
8 30089
 
6.9%
7 28656
 
6.6%
9 27873
 
6.4%
10 24044
 
5.5%
5 20464
 
4.7%
6 20290
 
4.6%
11 19979
 
4.6%
12 16472
 
3.8%
2 15363
 
3.5%
Other values (14) 154239
35.3%
ValueCountFrequency (%)
0 79407
18.2%
1 12958
 
3.0%
2 15363
 
3.5%
3 8897
 
2.0%
4 12145
 
2.8%
5 20464
 
4.7%
6 20290
 
4.6%
7 28656
 
6.6%
8 30089
 
6.9%
9 27873
 
6.4%
ValueCountFrequency (%)
23 11010
2.5%
22 9952
2.3%
21 10887
2.5%
20 11768
2.7%
19 10148
2.3%
18 9830
2.3%
17 9979
2.3%
16 10407
2.4%
15 10846
2.5%
14 11761
2.7%

SO2 AQI
Real number (ℝ)

High correlation  Zeros 

Distinct140
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1173857
Minimum0
Maximum200
Zeros95944
Zeros (%)22.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2025-07-15T05:16:45.356647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q39
95-th percentile29
Maximum200
Range200
Interquartile range (IQR)8

Descriptive statistics

Standard deviation11.939231
Coefficient of variation (CV)1.6774742
Kurtosis22.191961
Mean7.1173857
Median Absolute Deviation (MAD)3
Skewness3.8946971
Sum3109415
Variance142.54523
MonotonicityNot monotonic
2025-07-15T05:16:45.507136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 95944
22.0%
1 84476
19.3%
3 56660
13.0%
4 38148
 
8.7%
6 27578
 
6.3%
7 20695
 
4.7%
9 16086
 
3.7%
10 12774
 
2.9%
11 10850
 
2.5%
13 8689
 
2.0%
Other values (130) 64976
14.9%
ValueCountFrequency (%)
0 95944
22.0%
1 84476
19.3%
3 56660
13.0%
4 38148
 
8.7%
6 27578
 
6.3%
7 20695
 
4.7%
9 16086
 
3.7%
10 12774
 
2.9%
11 10850
 
2.5%
13 8689
 
2.0%
ValueCountFrequency (%)
200 2
< 0.1%
195 1
< 0.1%
180 1
< 0.1%
177 2
< 0.1%
176 2
< 0.1%
173 2
< 0.1%
172 1
< 0.1%
169 1
< 0.1%
168 2
< 0.1%
163 1
< 0.1%

CO Units
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.7 MiB
Parts per million
436876 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters7426892
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million 436876
100.0%

Length

2025-07-15T05:16:45.637552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-15T05:16:45.704928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
parts 436876
33.3%
per 436876
33.3%
million 436876
33.3%

Most occurring characters

ValueCountFrequency (%)
r 873752
11.8%
i 873752
11.8%
l 873752
11.8%
873752
11.8%
P 436876
 
5.9%
s 436876
 
5.9%
t 436876
 
5.9%
a 436876
 
5.9%
p 436876
 
5.9%
m 436876
 
5.9%
Other values (3) 1310628
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7426892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 873752
11.8%
i 873752
11.8%
l 873752
11.8%
873752
11.8%
P 436876
 
5.9%
s 436876
 
5.9%
t 436876
 
5.9%
a 436876
 
5.9%
p 436876
 
5.9%
m 436876
 
5.9%
Other values (3) 1310628
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7426892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 873752
11.8%
i 873752
11.8%
l 873752
11.8%
873752
11.8%
P 436876
 
5.9%
s 436876
 
5.9%
t 436876
 
5.9%
a 436876
 
5.9%
p 436876
 
5.9%
m 436876
 
5.9%
Other values (3) 1310628
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7426892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 873752
11.8%
i 873752
11.8%
l 873752
11.8%
873752
11.8%
P 436876
 
5.9%
s 436876
 
5.9%
t 436876
 
5.9%
a 436876
 
5.9%
p 436876
 
5.9%
m 436876
 
5.9%
Other values (3) 1310628
17.6%

CO
Real number (ℝ)

High correlation  Zeros 

Distinct3003
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3697229
Minimum-0.4375
Maximum7.508333
Zeros16904
Zeros (%)3.9%
Negative255
Negative (%)0.1%
Memory size6.7 MiB
2025-07-15T05:16:45.817528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.4375
5-th percentile0.025
Q10.191667
median0.295833
Q30.470833
95-th percentile0.95
Maximum7.508333
Range7.945833
Interquartile range (IQR)0.279166

Descriptive statistics

Standard deviation0.31636384
Coefficient of variation (CV)0.85567824
Kurtosis16.595442
Mean0.3697229
Median Absolute Deviation (MAD)0.133333
Skewness2.8311077
Sum161523.06
Variance0.10008608
MonotonicityNot monotonic
2025-07-15T05:16:45.969171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 26222
 
6.0%
0 16904
 
3.9%
0.1 15771
 
3.6%
0.3 11384
 
2.6%
0.4 5253
 
1.2%
0.233333 4112
 
0.9%
0.216667 3824
 
0.9%
0.229167 3682
 
0.8%
0.225 3679
 
0.8%
0.266667 3639
 
0.8%
Other values (2993) 342406
78.4%
ValueCountFrequency (%)
-0.4375 1
 
< 0.1%
-0.4 1
 
< 0.1%
-0.375 1
 
< 0.1%
-0.345833 1
 
< 0.1%
-0.333333 1
 
< 0.1%
-0.314286 1
 
< 0.1%
-0.304348 1
 
< 0.1%
-0.3 3
< 0.1%
-0.291667 1
 
< 0.1%
-0.2875 1
 
< 0.1%
ValueCountFrequency (%)
7.508333 1
< 0.1%
6.975 1
< 0.1%
6.533333 1
< 0.1%
6.5 1
< 0.1%
5.8875 1
< 0.1%
5.779167 1
< 0.1%
5.633333 1
< 0.1%
5.547059 1
< 0.1%
5.2625 1
< 0.1%
5.195833 1
< 0.1%

CO 1st Max Value
Real number (ℝ)

High correlation  Zeros 

Distinct111
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52922202
Minimum-0.4
Maximum15.5
Zeros17024
Zeros (%)3.9%
Negative61
Negative (%)< 0.1%
Memory size6.7 MiB
2025-07-15T05:16:46.115739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.4
5-th percentile0.1
Q10.2
median0.4
Q30.7
95-th percentile1.4
Maximum15.5
Range15.9
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.50996174
Coefficient of variation (CV)0.96360643
Kurtosis32.540721
Mean0.52922202
Median Absolute Deviation (MAD)0.2
Skewness3.8175992
Sum231204.4
Variance0.26006098
MonotonicityNot monotonic
2025-07-15T05:16:46.268101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 73002
16.7%
0.2 71307
16.3%
0.4 57487
13.2%
0.5 43727
10.0%
0.1 32334
7.4%
0.6 32283
7.4%
0.7 23505
 
5.4%
0.8 17636
 
4.0%
0 17024
 
3.9%
0.9 13325
 
3.1%
Other values (101) 55246
12.6%
ValueCountFrequency (%)
-0.4 2
 
< 0.1%
-0.3 5
 
< 0.1%
-0.2 18
 
< 0.1%
-0.1 36
 
< 0.1%
0 17024
 
3.9%
0.1 32334
7.4%
0.2 71307
16.3%
0.3 73002
16.7%
0.4 57487
13.2%
0.5 43727
10.0%
ValueCountFrequency (%)
15.5 1
< 0.1%
14.4 1
< 0.1%
13.8 1
< 0.1%
13 1
< 0.1%
12.4 1
< 0.1%
12 1
< 0.1%
11.7 1
< 0.1%
11.6 2
< 0.1%
11.5 1
< 0.1%
11 2
< 0.1%

CO 1st Max Hour
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2550953
Minimum0
Maximum23
Zeros194622
Zeros (%)44.5%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2025-07-15T05:16:47.176327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q310
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.8420189
Coefficient of variation (CV)1.253701
Kurtosis-0.24404115
Mean6.2550953
Median Absolute Deviation (MAD)2
Skewness1.0568389
Sum2732701
Variance61.497261
MonotonicityNot monotonic
2025-07-15T05:16:47.281694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 194622
44.5%
23 29890
 
6.8%
7 23268
 
5.3%
8 21779
 
5.0%
6 16936
 
3.9%
9 16335
 
3.7%
1 15864
 
3.6%
22 15195
 
3.5%
10 12522
 
2.9%
2 11826
 
2.7%
Other values (14) 78639
18.0%
ValueCountFrequency (%)
0 194622
44.5%
1 15864
 
3.6%
2 11826
 
2.7%
3 8815
 
2.0%
4 6910
 
1.6%
5 9653
 
2.2%
6 16936
 
3.9%
7 23268
 
5.3%
8 21779
 
5.0%
9 16335
 
3.7%
ValueCountFrequency (%)
23 29890
6.8%
22 15195
3.5%
21 9177
 
2.1%
20 6268
 
1.4%
19 4604
 
1.1%
18 3439
 
0.8%
17 2893
 
0.7%
16 2626
 
0.6%
15 2513
 
0.6%
14 2843
 
0.7%

CO AQI
Real number (ℝ)

High correlation  Zeros 

Distinct107
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9962575
Minimum0
Maximum201
Zeros17085
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2025-07-15T05:16:47.402188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q38
95-th percentile16
Maximum201
Range201
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.8515892
Coefficient of variation (CV)0.97587356
Kurtosis32.347733
Mean5.9962575
Median Absolute Deviation (MAD)3
Skewness3.6725685
Sum2619621
Variance34.241096
MonotonicityNot monotonic
2025-07-15T05:16:47.559864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 73001
16.7%
2 71306
16.3%
5 57432
13.1%
6 43729
10.0%
1 32334
7.4%
7 32284
7.4%
8 23504
 
5.4%
9 17636
 
4.0%
0 17085
 
3.9%
10 13325
 
3.1%
Other values (97) 55240
12.6%
ValueCountFrequency (%)
0 17085
 
3.9%
1 32334
7.4%
2 71306
16.3%
3 73001
16.7%
5 57432
13.1%
6 43729
10.0%
7 32284
7.4%
8 23504
 
5.4%
9 17636
 
4.0%
10 13325
 
3.1%
ValueCountFrequency (%)
201 1
< 0.1%
183 1
< 0.1%
173 1
< 0.1%
159 1
< 0.1%
150 1
< 0.1%
143 1
< 0.1%
138 1
< 0.1%
136 2
< 0.1%
135 1
< 0.1%
126 2
< 0.1%

Interactions

2025-07-15T05:16:30.430937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:22.773285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:25.993819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:28.941662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:33.494388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:36.826276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:40.285110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:43.886301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:48.620767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:51.833484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:55.261142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:00.245661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:03.788775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:07.051158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:10.671944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:15.166651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:18.526257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:21.851096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:26.227212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:30.637209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:22.946312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:26.142306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:29.138295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:33.695702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:37.011581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:40.488236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:44.162956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:48.801556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:52.030602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:55.454178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:00.441566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:03.986936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:07.256475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:10.918154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:15.379468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:18.720943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:22.040930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:26.413372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:30.829779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:23.097761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:26.289242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:29.315131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:33.882390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:37.190921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:40.682944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:44.440513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:48.946297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:52.252389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:55.651048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:00.626981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:04.176890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:07.443858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:11.195086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:15.575437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:18.913777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:22.237689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:26.595642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:31.015438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:23.293855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:26.439334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:29.452926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:34.068627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:37.368841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:40.866210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:44.713075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:49.150377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:52.443760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:55.836273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:00.807375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:04.383382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:07.628210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:11.453052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:15.769563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:19.092299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:22.418867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:26.779615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:31.212307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:23.459575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:26.599510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:29.599920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:34.280135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:37.515710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:41.054410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:44.955418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:49.306376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:52.604637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:56.022352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:00.997950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:04.540913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:07.791649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:11.717674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:15.923892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:19.250556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:22.583991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:26.973238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:31.375908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:23.609150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:26.759660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:29.771794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:34.439724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:37.669985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:41.233856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:45.224387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:49.460516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:52.773274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:56.207909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:01.175403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:04.690767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:07.954963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:11.983799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:16.088286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:19.407025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:22.814482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:27.152550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:31.564201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:23.766701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:26.908735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:29.948896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:34.621242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:37.841068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:41.407441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:45.496865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:49.639648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:52.923922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:56.400135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:01.357720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:04.897765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:08.139453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:12.834308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:16.277804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:19.606234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:23.089444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:27.342061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:31.755178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:23.916553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:27.066636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:30.119141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:34.808565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:38.290942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:41.609517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:45.776803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:49.820754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:53.136539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:56.674314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:01.542807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:05.072205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:08.335240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:13.011297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:16.476187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:19.794606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:23.348307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:27.529009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:31.906851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:24.070331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:27.213940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:30.350879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:34.964441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:38.458211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:41.791195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:46.064469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:49.968769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:53.320009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:56.917092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:01.728261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:05.229205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:08.502116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:13.189530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:16.626248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:19.945705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:23.580843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:27.706612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:32.078437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:24.238443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:27.365776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:30.568978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:35.133628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:38.621053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:41.947495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:46.354565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:50.144626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:53.495888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:57.209749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:01.930894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:05.387202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:08.669271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:13.406097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:16.790148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:20.107323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:23.843605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:27.901287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:32.278471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:24.380340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:27.502987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:30.802217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:35.295597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:38.799990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:42.090092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:46.549325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:50.325448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:53.689147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:57.475602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:02.107993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:05.559156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:08.862216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:13.578198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:16.974145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:20.296693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:24.103990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:28.081732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:32.463105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:24.528367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:27.642448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:31.322244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:35.478302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:38.970731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:42.272612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:46.736897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:50.508907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:53.879398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:57.732801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:02.288264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:05.732820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:09.045898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:13.743992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:17.151722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:20.470784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:24.352287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:28.262108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:32.625753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:24.690173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:27.790685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:31.589361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:35.637513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:39.114087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:42.424902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:46.933501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:50.672863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:54.044144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:57.972250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:02.475740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:05.889306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:09.207920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:13.917586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:17.318429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:20.653555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:24.593892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:29.076832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:32.783657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:24.858442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:27.961572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:31.861098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:35.799384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:39.276457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:42.596264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:47.127954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:50.835644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:54.222894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:58.832802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:02.667691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:06.040498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:09.363024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:14.090416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:17.502095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:20.812196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:24.855762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:29.270558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:32.965899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:25.005606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:28.105911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:32.159575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:35.979261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:39.451686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:42.769993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:47.651310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:51.022506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:54.407656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:59.096056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:02.846730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:06.218561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:09.536052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:14.266558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:17.681135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:20.999693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:25.153347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:29.448827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:33.120585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:25.167490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:28.264284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:32.440082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:36.145659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:39.622706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:42.964849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:47.842661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:51.188331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:54.569923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:59.407459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:03.035485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:06.377240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:09.758758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:14.455853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:17.840323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:21.178469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:25.412627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:29.645065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:33.292980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:25.322263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:28.418062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:32.724719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:36.307572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:39.769320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:43.150099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:48.046574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:51.335572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:54.736636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:59.655967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:03.213704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:06.531143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:09.978319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:14.632677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:17.993547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:21.333914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:25.677306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:29.844285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:33.452410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:25.485294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:28.587164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:33.026265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:36.480531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:39.927165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:43.341180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:48.241103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:51.495341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:54.907769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:59.858649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:03.410261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:06.688436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:10.221925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:14.815924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:18.158658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:21.496663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:25.852461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:30.045473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:33.632710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:25.636542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:28.765824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:33.299216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:36.662379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:40.114291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:43.599067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:48.429788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:51.679685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:15:55.081576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:00.045388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:03.586123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:06.879218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:10.456293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:14.985391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:18.350477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:21.695020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:26.060373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-15T05:16:30.241402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-15T05:16:47.699307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
COCO 1st Max HourCO 1st Max ValueCO AQICounty CodeNO2NO2 1st Max HourNO2 1st Max ValueNO2 AQIO3O3 1st Max HourO3 1st Max ValueO3 AQISO2SO2 1st Max HourSO2 1st Max ValueSO2 AQISite NumStateState Code
CO1.0000.2400.9480.948-0.0950.5860.0390.5340.535-0.316-0.025-0.148-0.1600.2960.0510.2780.279-0.0040.131-0.278
CO 1st Max Hour0.2401.0000.3560.356-0.0160.3480.2250.3730.373-0.218-0.058-0.078-0.0820.1310.1210.1410.1410.0230.091-0.077
CO 1st Max Value0.9480.3561.0001.000-0.0820.6640.0650.6360.637-0.358-0.038-0.149-0.1620.3140.0710.3060.3060.0080.100-0.255
CO AQI0.9480.3561.0001.000-0.0820.6640.0650.6360.637-0.358-0.038-0.150-0.1620.3140.0710.3060.3070.0080.075-0.255
County Code-0.095-0.016-0.082-0.0821.000-0.074-0.005-0.064-0.0650.0580.0190.0560.056-0.055-0.031-0.032-0.025-0.1170.5160.203
NO20.5860.3480.6640.664-0.0741.0000.1200.9280.928-0.423-0.002-0.132-0.1440.4430.1490.4600.4550.0330.134-0.088
NO2 1st Max Hour0.0390.2250.0650.065-0.0050.1201.0000.1570.157-0.182-0.091-0.065-0.0660.0330.1550.0300.027-0.0240.0940.001
NO2 1st Max Value0.5340.3730.6360.636-0.0640.9280.1571.0001.000-0.310-0.002-0.010-0.0210.4030.1470.4300.4240.0480.123-0.078
NO2 AQI0.5350.3730.6370.637-0.0650.9280.1571.0001.000-0.309-0.002-0.010-0.0210.4060.1450.4330.4280.0480.152-0.079
O3-0.316-0.218-0.358-0.3580.058-0.423-0.182-0.310-0.3091.0000.1540.8610.861-0.127-0.090-0.123-0.1180.0300.0870.038
O3 1st Max Hour-0.025-0.058-0.038-0.0380.019-0.002-0.091-0.002-0.0020.1541.0000.1930.1930.026-0.0020.0400.0400.0180.0970.038
O3 1st Max Value-0.148-0.078-0.149-0.1500.056-0.132-0.065-0.010-0.0100.8610.1931.0000.995-0.013-0.0210.0100.0130.0510.0920.022
O3 AQI-0.160-0.082-0.162-0.1620.056-0.144-0.066-0.021-0.0210.8610.1930.9951.000-0.032-0.017-0.012-0.0120.0470.0840.025
SO20.2960.1310.3140.314-0.0550.4430.0330.4030.406-0.1270.026-0.013-0.0321.0000.2330.9130.898-0.1230.0230.098
SO2 1st Max Hour0.0510.1210.0710.071-0.0310.1490.1550.1470.145-0.090-0.002-0.021-0.0170.2331.0000.3250.281-0.0710.1060.116
SO2 1st Max Value0.2780.1410.3060.306-0.0320.4600.0300.4300.433-0.1230.0400.010-0.0120.9130.3251.0000.991-0.0940.0610.154
SO2 AQI0.2790.1410.3060.307-0.0250.4550.0270.4240.428-0.1180.0400.013-0.0120.8980.2810.9911.000-0.0810.1170.142
Site Num-0.0040.0230.0080.008-0.1170.033-0.0240.0480.0480.0300.0180.0510.047-0.123-0.071-0.094-0.0811.0000.462-0.183
State0.1310.0910.1000.0750.5160.1340.0940.1230.1520.0870.0970.0920.0840.0230.1060.0610.1170.4621.0001.000
State Code-0.278-0.077-0.255-0.2550.203-0.0880.001-0.078-0.0790.0380.0380.0220.0250.0980.1160.1540.142-0.1831.0001.000

Missing values

2025-07-15T05:16:34.031267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-15T05:16:35.216493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2NO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3O3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2SO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCOCO 1st Max ValueCO 1st Max HourCO AQI
141330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-01Parts per billion19.04166749.01946Parts per million0.0225000.0401034Parts per billion3.0000009.02113.0Parts per million0.8789472.22325.0
541330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-02Parts per billion22.95833336.01934Parts per million0.0133750.0321027Parts per billion1.9583333.0224.0Parts per million1.0666672.3026.0
941330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-03Parts per billion38.12500051.0848Parts per million0.0079580.016914Parts per billion5.25000011.01916.0Parts per million1.7625002.5828.0
1341330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-04Parts per billion40.26087074.0872Parts per million0.0141670.033928Parts per billion7.08333316.0823.0Parts per million1.8291673.02334.0
1741330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-05Parts per billion48.45000061.02258Parts per million0.0066670.012910Parts per billion8.70833315.0721.0Parts per million2.7000003.7242.0
2141330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-06Parts per billion39.95000073.0871Parts per million0.0117500.0251021Parts per billion6.76190517.0724.0Parts per million2.3083333.6941.0
2541330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-07Parts per billion29.62500043.0941Parts per million0.0116250.0241020Parts per billion8.66666721.0730.0Parts per million1.8291673.52340.0
2941330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-08Parts per billion29.66666741.0039Parts per million0.0097500.0201017Parts per billion8.25000018.0026.0Parts per million2.7875005.1257.0
3341330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-09Parts per billion25.08333337.02035Parts per million0.0107920.0221019Parts per billion6.50000013.01919.0Parts per million1.6750002.8232.0
3741330021645 E ROOSEVELT ST-CENTRAL PHOENIX STNArizonaMaricopaPhoenix2000-01-10Parts per billion37.66666770.02068Parts per million0.0084580.015913Parts per billion9.95833321.02030.0Parts per million2.1791673.72342.0
State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2NO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3O3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2SO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCOCO 1st Max ValueCO 1st Max HourCO AQI
17466225621100NCore - North Cheyenne Soccer ComplexWyomingLaramieNot in a city2016-03-22Parts per billion4.82500026.5825Parts per million0.0376670.0511047Parts per billion0.0913041.081.0Parts per million0.1000000.101.0
17466265621100NCore - North Cheyenne Soccer ComplexWyomingLaramieNot in a city2016-03-23Parts per billion1.2739132.072Parts per million0.0351250.0422239Parts per billion0.0000000.140.0Parts per million0.1000000.101.0
17466305621100NCore - North Cheyenne Soccer ComplexWyomingLaramieNot in a city2016-03-24Parts per billion2.2125008.9238Parts per million0.0447920.0491045Parts per billion-0.0833330.060.0Parts per million0.1000000.101.0
17466345621100NCore - North Cheyenne Soccer ComplexWyomingLaramieNot in a city2016-03-25Parts per billion1.62608710.909Parts per million0.0417080.049845Parts per billion-0.0318180.100.0Parts per million0.1000000.101.0
17466385621100NCore - North Cheyenne Soccer ComplexWyomingLaramieNot in a city2016-03-26Parts per billion3.75833327.62125Parts per million0.0332920.0411038Parts per billion-0.0500000.330.0Parts per million0.1000000.101.0
17466425621100NCore - North Cheyenne Soccer ComplexWyomingLaramieNot in a city2016-03-27Parts per billion4.27727323.52322Parts per million0.0419580.0501046Parts per billion-0.0952380.000.0Parts per million0.1000000.101.0
17466465621100NCore - North Cheyenne Soccer ComplexWyomingLaramieNot in a city2016-03-28Parts per billion8.31739122.6621Parts per million0.0412920.052948Parts per billion0.1173910.570.0Parts per million0.1000000.101.0
17466505621100NCore - North Cheyenne Soccer ComplexWyomingLaramieNot in a city2016-03-29Parts per billion2.5647063.663Parts per million0.0280000.0402337Parts per billion0.1437500.780.0Parts per million0.0066670.101.0
17466545621100NCore - North Cheyenne Soccer ComplexWyomingLaramieNot in a city2016-03-30Parts per billion1.0833331.691Parts per million0.0439170.0481844Parts per billion0.0166670.100.0Parts per million0.0916670.121.0
17466585621100NCore - North Cheyenne Soccer ComplexWyomingLaramieNot in a city2016-03-31Parts per billion0.9391301.351Parts per million0.0452630.047944Parts per billion-0.0227270.000.0Parts per million0.1000000.101.0

Duplicate rows

Most frequently occurring

State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2NO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3O3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2SO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCOCO 1st Max ValueCO 1st Max HourCO AQI# duplicates
429252542HARRISON AVEMassachusettsSuffolkBoston2002-06-10Parts per billion21.31818239.01837Parts per million0.0233890.0302025Parts per billion6.20833324.0634.0Parts per million0.3333330.5131.024
417252542HARRISON AVEMassachusettsSuffolkBoston2002-06-09Parts per billion19.20833356.02353Parts per million0.0361900.0511043Parts per billion2.2083334.0236.0Parts per million0.1875000.4236.016
425252542HARRISON AVEMassachusettsSuffolkBoston2002-06-10Parts per billion18.35714339.01837Parts per million0.0233890.0302025Parts per billion6.20833324.0634.0Parts per million0.3333330.5131.012
427252542HARRISON AVEMassachusettsSuffolkBoston2002-06-10Parts per billion21.31818239.01837Parts per million0.0233890.0302025Parts per billion4.25000011.0834.0Parts per million0.3333330.5131.012
430252542HARRISON AVEMassachusettsSuffolkBoston2002-06-10Parts per billion21.31818239.01837Parts per million0.0233890.0302025Parts per billion6.20833324.0634.0Parts per million1.6733332.7531.012
410252542HARRISON AVEMassachusettsSuffolkBoston2002-06-09Parts per billion17.60869644.02253Parts per million0.0361900.0511043Parts per billion2.2083334.0236.0Parts per million0.1875000.4236.08
415252542HARRISON AVEMassachusettsSuffolkBoston2002-06-09Parts per billion19.20833356.02353Parts per million0.0361900.0511043Parts per billion2.1304353.076.0Parts per million0.1875000.4236.08
418252542HARRISON AVEMassachusettsSuffolkBoston2002-06-09Parts per billion19.20833356.02353Parts per million0.0361900.0511043Parts per billion2.2083334.0236.0Parts per million0.1916670.5236.08
421252542HARRISON AVEMassachusettsSuffolkBoston2002-06-09Parts per billion19.20833356.02353Parts per million0.0370000.0511043Parts per billion2.2083334.0236.0Parts per million0.1875000.4236.08
423252542HARRISON AVEMassachusettsSuffolkBoston2002-06-10Parts per billion18.35714339.01837Parts per million0.0233890.0302025Parts per billion4.25000011.0834.0Parts per million0.3333330.5131.06